№83-28
Role of robotic systems in optimizing drilling processes in the mining industry
Ye. Koroviaka1, https://orcid.org/0000-0002-2675-6610
O. Pashchenko1, https://orcid.org/0000-0003-3296-996X
V. Yavorska1, https://orcid.org/0000-0002-1639-6818
A. Shumov1, https://orcid.org/0000-0002-8856-4753
D. Zybalov1 https://orcid.org/0000-0001-5891-9325
1Dnipro University of Technology, Dnipro, Ukraine
Coll.res.pap.nat.min.univ. 2025, 83:315–323
Full text (PDF)
https://doi.org/10.33271/crpnmu/83.315
ABSTRACT
Purpose. Justification and analysis of innovative solutions to improve the efficiency, safety, and environmental sustainability of rock breaking processes in the mining industry.
Methodology. This study analyzes the role of robotic systems in optimizing rock breaking processes, with a focus on the integration of artificial intelligence (AI) and sensors. A mathematical model of the process is proposed, taking into account drilling speed, accuracy, and energy consumption, and validated using simulated and partially real mining data.
Findings. The proposed model integrates robotic systems with AI and hyperspectral analysis, demonstrating a 15% increase in productivity, a 10% reduction in energy consumption, and a 12% decrease in emissions compared to conventional methods. This approach also enhances safety by reducing workplace injuries by approximately 30%, aligning with the industry’s shift toward automation. The study’s comparative analysis underscores the superiority of automated methods over traditional techniques, offering a pathway to more sustainable mining practices.
The originality. Relationships have been established between the level of integration of robotic systems with artificial intelligence algorithms and sensor technologies and rock crushing efficiency. The implementation of such systems increases productivity, reduces energy consumption and emissions, and improves safety by reducing injuries.
Practical implementation. Practical implications suggest the adoption of this model by major mining companies such as BHP and Rio Tinto, which could leverage these advancements to improve operational efficiency and meet sustainability goals. The study acknowledges limitations, particularly the reliance on simulated data due to limited access to real-time field trials, and recommends future research into quantum sensors for enhanced localization in underground settings. This research contributes to the evolving field of mining automation, offering a foundation for further technological integration and industrial application.
Keywords: robotic systems, automation, rock fragmentation, mining industry, efficiency, safety, artificial intelligence, sensors, hyperspectral analysis, sustainability.
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date of first submission of the article to the publication – 10/01/2025
date of acceptance of the article for publication after review – 11/06/2025
date of publication – 12/29/2025